# Difference between revisions of "VS265: Reading Fall2010"

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* [http://redwood.berkeley.edu/vs265/superlearn_handout2.pdf Handout] on supervised learning in multi-layer feedforward networks - "backpropagation" | * [http://redwood.berkeley.edu/vs265/superlearn_handout2.pdf Handout] on supervised learning in multi-layer feedforward networks - "backpropagation" | ||

* Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) [http://redwood.berkeley.edu/vs265/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.). | * Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) [http://redwood.berkeley.edu/vs265/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.). | ||

− | * [http:// | + | * [http://cnl.salk.edu/Research/ParallelNetsPronounce/ NetTalk demo] |

+ | |||

+ | ==== 21 Sep ==== | ||

+ | * Handout: [http://redwood.berkeley.edu/vs265/hebb-pca.pdf Hebbian learning and PCA] | ||

+ | * '''HKP''' Chapters 8 and 9 | ||

+ | * '''PDP''' [http://redwood.berkeley.edu/vs265/chap9.pdf Chapter 9] (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors) | ||

+ | |||

+ | Optional: | ||

+ | * Atick, Redlich. [http://redwood.berkeley.edu/vs265/Atick-Redlich-NC92.pdf What does the retina know about natural scenes?], Neural Computation, 1992. | ||

+ | * Dan, Atick, Reid. [http://www.jneurosci.org/cgi/reprint/16/10/3351.pdf Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory], J Neuroscience, 1996. |

## Revision as of 22:19, 21 September 2010

#### 26 Aug

- Dreyfus, H.L. and Dreyfus, S.E.
*Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint*. Daedalus, Winter 1988. - Bell, A.J.
*Levels and loops: the future of artificial intelligence and neuroscience*. Phil Trans: Bio Sci.**354**:2013--2020 (1999) here or here

Optional:

- Land, MF and Fernald, RD. The Evolution of Eyes, Ann Revs Neuro, 1992.

#### 31 Aug

- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from
*Analog VLSI and Neural Systems*, Addison-Wesley, 1989. - Linear time-invariant systems and convolution
- Simulating differential equations
- Dynamics
- Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.

#### 02 Sep

- Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart,
*Parallel Distributed Processing*, MIT Press, 1985. - Linear neuron models
- Linear algebra primer

#### 07 Sep

- Handout on supervised learning in single-stage feedforward networks
- Handout on supervised learning in multi-layer feedforward networks - "backpropagation"
- Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) "Efficient BackProp," in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
- NetTalk demo

#### 21 Sep

- Handout: Hebbian learning and PCA
**HKP**Chapters 8 and 9**PDP**Chapter 9 (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)

Optional:

- Atick, Redlich. What does the retina know about natural scenes?, Neural Computation, 1992.
- Dan, Atick, Reid. Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory, J Neuroscience, 1996.